
# Ignore warnings
import warnings
warnings.filterwarnings('ignore')

import numpy as np
import pandas as pd
import seaborn as sns
from datetime import datetime
import matplotlib.pyplot as plt
import plotly.express as px
from scipy.stats import linregress
from sklearn.ensemble import IsolationForest
from matplotlib.colors import LinearSegmentedColormap
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer
import matplotlib.gridspec as gridspec
from sklearn.metrics import silhouette_score, calinski_harabasz_score, davies_bouldin_score
from collections import Counter
import plotly.graph_objects as go
from tabulate import tabulate


%matplotlib inline


df = pd.read_csv("online_shopping_dataset.csv")


df.head()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 16 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   UserID             1000 non-null   object 
 1   Age                1000 non-null   int64  
 2   Gender             1000 non-null   object 
 3   Location           1000 non-null   object 
 4   Item Name          1000 non-null   object 
 5   Brand              1000 non-null   object 
 6   Price              1000 non-null   float64
 7   Rating             1000 non-null   float64
 8   Number of Reviews  1000 non-null   int64  
 9   Purchase History   1000 non-null   bool   
 10  Search History     1000 non-null   bool   
 11  Wishlist           1000 non-null   bool   
 12  Cart Items         1000 non-null   bool   
 13  Click History      1000 non-null   bool   
 14  Transaction No.    1000 non-null   int64  
 15  Transaction Date   1000 non-null   object 
dtypes: bool(5), float64(2), int64(3), object(6)
memory usage: 90.9+ KB


# Summary statistics for numerical variables
df.describe().T


# Summary statistics for categorical variables
df.describe(include='object').T


df.isnull().sum()

UserID               0
Age                  0
Gender               0
Location             0
Item Name            0
Brand                0
Price                0
Rating               0
Number of Reviews    0
Purchase History     0
Search History       0
Wishlist             0
Cart Items           0
Click History        0
Transaction No.      0
Transaction Date     0
dtype: int64


# Finding duplicate rows
duplicate_rows = df[df.duplicated(keep=False)]

# Displaying the duplicate rows
duplicate_rows.head()


# Displaying the number of duplicate rows
print(f"The dataset contains {df.duplicated().sum()} duplicate rows that need to be removed.")

The dataset contains 0 duplicate rows that need to be removed.

df


# Convert 'Purchase History' column to boolean values
df['Purchase History'] = df['Purchase History'].astype(bool)

# Count 'Transaction No.' as 0 if 'Purchase History' is False
df['Transaction No.'] = df.apply(lambda row: 0 if not row['Purchase History'] else row['Transaction No.'], axis=1)

df


# Numerical columns
numerical_cols = ['Age', 'Price', 'Rating', 'Number of Reviews']
descriptive_stats_numerical = df[numerical_cols].describe()


descriptive_stats_numerical


# Histograms for numerical variables
for col in numerical_cols:
    plt.figure(figsize=(8, 6))
    plt.hist(df[col], bins=10, edgecolor='black')
    plt.title(f'Histogram of {col}')
    plt.xlabel(col)
    plt.ylabel('Frequency')
    plt.show()


bin1 = df[df['Age'] <= 22]['UserID'].count()

print("Number of UserID with Age below or equal to 22:", bin1)

Number of UserID with Age below or equal to 22: 101


bin2 = df[(df['Age'] >= 23) & (df['Age'] <= 27)]['UserID'].count()

print("Number of UserID with Age more or equal to 23 to less or equal to 27:", bin2)

Number of UserID with Age more or equal to 23 to less or equal to 27: 98


# Categorical columns
categorical_cols = ['Gender', 'Location', 'Item Name', 'Brand', 'Purchase History', 'Search History', 'Wishlist', 'Cart Items', 'Click History']
descriptive_stats_categorical = df[categorical_cols].describe()


descriptive_stats_categorical


# Count the frequency of unique values in categorical columns
frequency_categorical = {col: df[col].value_counts() for col in categorical_cols}


frequency_categorical

{'Gender': Male      530
 Female    470
 Name: Gender, dtype: int64,
 'Location': Greece         74
 Russia         71
 India          65
 New Zealand    62
 Japan          61
 USA            59
 Brazil         58
 UK             55
 Singapore      55
 France         54
 Argentina      53
 Belgium        52
 Switzerland    50
 Italy          49
 Australia      49
 Canada         46
 Spain          44
 UAE            43
 Name: Location, dtype: int64,
 'Item Name': T-Shirt       124
 Smartphone    115
 Shoes         109
 Headphones    104
 Laptop         99
 Bag            96
 Jeans          93
 Perfume        93
 Sunglasses     86
 Watch          81
 Name: Item Name, dtype: int64,
 'Brand': Smith Inc                         3
 Martin Group                      3
 Hernandez Ltd                     3
 Martin and Sons                   3
 Smith Group                       3
                                  ..
 Anderson, Mcpherson and Miller    1
 Johnson, Murphy and Ruiz          1
 Ryan, Hudson and Best             1
 Osborne, Cooper and Green         1
 Wright-Richards                   1
 Name: Brand, Length: 969, dtype: int64,
 'Purchase History': False    501
 True     499
 Name: Purchase History, dtype: int64,
 'Search History': True     515
 False    485
 Name: Search History, dtype: int64,
 'Wishlist': True     506
 False    494
 Name: Wishlist, dtype: int64,
 'Cart Items': True     522
 False    478
 Name: Cart Items, dtype: int64,
 'Click History': True     514
 False    486
 Name: Click History, dtype: int64}


# Bar plots and count plots for categorical variables
for col in categorical_cols:
    plt.figure(figsize=(10, 6))
    if len(df[col].unique()) <= 10:  # For variables with fewer unique values, use countplot
        sns.countplot(data=df, x=col)
        plt.title(f'Count Plot of {col}')
        plt.xlabel(col)
        plt.ylabel('Frequency')
        plt.xticks(rotation=45)
    else:  # For variables with more than 10 unique values, use bar plot
        df[col].value_counts().plot(kind='bar')
        plt.title(f'Bar Plot of {col}')
        plt.xlabel(col)
        plt.ylabel('Frequency')
        plt.xticks(rotation=45)
    plt.show()

df


# Convert Transaction Date to datetime type
df['Transaction Date'] = pd.to_datetime(df['Transaction Date'])

# Convert Transaction Date to datetime and extract only the date
df['Transaction Day'] = df['Transaction Date'].dt.date

# Find the most recent purchase date for each customer
customer_engagement = df.groupby('UserID')['Transaction Day'].max().reset_index()

# Find the most recent date in the entire dataset
most_recent_date = df['Transaction Day'].max()

# Convert Transaction Day to datetime type before subtraction
customer_engagement['Transaction Day'] = pd.to_datetime(customer_engagement['Transaction Day'])
most_recent_date = pd.to_datetime(most_recent_date)

# Calculate the number of days since the last purchase for each customer
customer_engagement['Days_Since_Last_Purchase'] = (most_recent_date - customer_engagement['Transaction Day']).dt.days

# Remove the Transaction Day column
customer_engagement.drop(columns=['Transaction Day'], inplace=True)


customer_engagement


# Filter out rows where 'Transaction No.' is not 0
filtered_df = df[df['Transaction No.'] != 0]

# Find total transactions for each customer
total_transactions = filtered_df.groupby('UserID')['Transaction No.'].nunique()

# Rename the column to 'Total Transactions'
total_transactions = total_transactions.reset_index().rename(columns={'Transaction No.': 'Total Transactions'})


# Merge the new features into the customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, total_transactions, on='UserID')


customer_engagement


# Assuming Purchase History column contains boolean values
# Convert True/False to 1/0
df['Purchase History'] = df['Purchase History'].astype(int)

# Group by UserID and sum the Purchase History column
Total_Products_Purchased = df.groupby('UserID')['Purchase History'].sum().reset_index()

# Rename the column to Total Products Purchased
Total_Products_Purchased.rename(columns={'Purchase History': 'Total Products Purchased'}, inplace=True)


# Merge the new features into the customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, Total_Products_Purchased, on='UserID')


customer_engagement


# Assuming Search History column contains boolean values
# Convert True/False to 1/0
df['Search History'] = df['Search History'].astype(int)

# Group by UserID and sum the Search History column
Total_Products_Searched = df.groupby('UserID')['Search History'].sum().reset_index()

# Rename the column to Total Products Searched
Total_Products_Searched.rename(columns={'Search History': 'Total Products Searched'}, inplace=True)


# Merge the new features into the customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, Total_Products_Searched, on='UserID')


customer_engagement


# Assuming Wishlist column contains boolean values
# Convert True/False to 1/0
df['Wishlist'] = df['Wishlist'].astype(int)

# Group by UserID and sum the Wishlist column
Total_Products_in_Wishlist = df.groupby('UserID')['Wishlist'].sum().reset_index()

# Rename the column to Total Products in Wishlist
Total_Products_in_Wishlist.rename(columns={'Wishlist': 'Total Products in Wishlist'}, inplace=True)


# Merge the new features into the customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, Total_Products_in_Wishlist, on='UserID')


customer_engagement


# Assuming Cart Items column contains boolean values
# Convert True/False to 1/0
df['Cart Items'] = df['Cart Items'].astype(int)

# Group by UserID and sum the Cart Items column
Total_Products_in_Cart = df.groupby('UserID')['Cart Items'].sum().reset_index()

# Rename the column to Total Products in Cart
Total_Products_in_Cart.rename(columns={'Cart Items': 'Total Products in Cart'}, inplace=True)


# Merge the new features into the customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, Total_Products_in_Cart, on='UserID')


customer_engagement


# Assuming Cart Items column contains boolean values
# Convert True/False to 1/0
df['Click History'] = df['Click History'].astype(int)

# Group by UserID and sum the Click History column
Total_Products_in_Click_History = df.groupby('UserID')['Click History'].sum().reset_index()

# Rename the column to Total Products in Click History
Total_Products_in_Click_History.rename(columns={'Click History': 'Total Products in Click History'}, inplace=True)


# Merge the new features into the customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, Total_Products_in_Click_History, on='UserID')


customer_engagement


# Calculating total spend for each customer
df['Total Spend'] = df['Price'] * df['Purchase History']

# Grouping by UserID and summing up total spend
total_spend = df.groupby('UserID')['Total Spend'].sum()


# Merge the new features into the customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, total_spend, on='UserID')


# Drop the 'Total Spend' column from the original dataframe 'df'
df.drop('Total Spend', axis=1, inplace=True)


customer_engagement


# Calculating total amount willing to spend for each customer
df['Willing to Spend'] = df['Price'] * (df['Wishlist'] | df['Cart Items'])

# Grouping by UserID and summing up total spend
total_willing_to_spend = df.groupby('UserID')['Willing to Spend'].sum()


# Merge the new features into the customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, total_willing_to_spend, on='UserID')


# Drop the 'Willing to Spend' column from the original dataframe 'df'
df.drop('Willing to Spend', axis=1, inplace=True)


customer_engagement


# Calculate average transaction value
customer_engagement['Average Transaction Value'] = customer_engagement['Total Spend'] / customer_engagement['Total Transactions']


customer_engagement


# Filter rows with Purchase History as 1 (True)
df_filtered = df[df['Purchase History'] == 1]

# Count unique products purchased by each customer
unique_products_purchased = df_filtered.groupby('UserID')['Item Name'].nunique()

# Rename the resulting Series to Unique Products Purchased
unique_products_purchased = unique_products_purchased.rename('Unique Products Purchased')


# Merge the new features into the customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, unique_products_purchased, on='UserID')


customer_engagement


# Combine Wishlist and Cart Items into one column
df['Willing to Purchase'] = df['Wishlist'] | df['Cart Items']

# Filter rows where the customer is willing to purchase
df_willing_to_purchase = df[df['Willing to Purchase'] == 1]

# Count unique products the customer is willing to purchase
unique_products_willing_to_purchase = df_willing_to_purchase.groupby('UserID')['Item Name'].nunique()

# Rename the resulting Series to Unique Products to be Purchased
unique_products_willing_to_purchase = unique_products_willing_to_purchase.rename('Unique Products to be Purchased')


# Merge the new features into the customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, unique_products_willing_to_purchase, on='UserID')


# Drop the 'Willing to Purchase' column from the original dataframe 'df'
df.drop('Willing to Purchase', axis=1, inplace=True)


customer_engagement


df['Age'].value_counts(normalize=True)

47    0.031
55    0.030
43    0.028
65    0.028
61    0.027
28    0.027
40    0.026
30    0.025
18    0.025
33    0.025
21    0.024
46    0.024
53    0.024
49    0.023
25    0.023
39    0.022
42    0.022
34    0.022
56    0.021
58    0.021
29    0.021
54    0.021
19    0.021
48    0.020
27    0.020
62    0.020
37    0.020
38    0.020
23    0.019
41    0.019
24    0.019
60    0.019
52    0.019
31    0.018
51    0.018
63    0.018
32    0.018
35    0.018
36    0.018
50    0.017
26    0.017
22    0.016
64    0.016
57    0.016
20    0.015
59    0.015
44    0.014
45    0.010
Name: Age, dtype: float64


df['Gender'].value_counts(normalize=True)

Male      0.53
Female    0.47
Name: Gender, dtype: float64


df['Item Name'].value_counts(normalize=True)

T-Shirt       0.124
Smartphone    0.115
Shoes         0.109
Headphones    0.104
Laptop        0.099
Bag           0.096
Jeans         0.093
Perfume       0.093
Sunglasses    0.086
Watch         0.081
Name: Item Name, dtype: float64


df['Location'].value_counts(normalize=True)

Greece         0.074
Russia         0.071
India          0.065
New Zealand    0.062
Japan          0.061
USA            0.059
Brazil         0.058
UK             0.055
Singapore      0.055
France         0.054
Argentina      0.053
Belgium        0.052
Switzerland    0.050
Italy          0.049
Australia      0.049
Canada         0.046
Spain          0.044
UAE            0.043
Name: Location, dtype: float64


# Demographic Features
# Set age based on majority
majority_age = df['Age'].mode()[0]
# Count customers with the same age or above
customer_engagement['Same_Age_or_Above'] = df['Age'].apply(lambda x: 1 if x >= majority_age else 0)
# Count Male customers
customer_engagement['Is_Male'] = df['Gender'].apply(lambda x: 1 if x == 'Male' else 0)

# Product-based Features
# Set item name based on majority
majority_item = df['Item Name'].mode()[0]
# Count customers buying the same item
customer_engagement['Buying_Same_Item'] = df['Item Name'].apply(lambda x: 1 if x == majority_item else 0)

# Geographic Features
# Set location based on majority
majority_location = df['Location'].mode()[0]
# Count customers from the same location
customer_engagement['From_Same_Location'] = df['Location'].apply(lambda x: 1 if x == majority_location else 0)


majority_age

47


majority_item

'T-Shirt'


majority_location

'Greece'


customer_engagement


# Assuming 'Transaction Date' is in datetime format
df['Transaction Date'] = pd.to_datetime(df['Transaction Date'])

# Group transactions by UserID and calculate the time difference between consecutive purchases
df['Days_Between_Purchases'] = df.groupby('UserID')['Transaction Date'].diff().dt.days

# Calculate average days between purchases for each user
average_days_between_purchases = df.groupby('UserID')['Days_Between_Purchases'].mean().reset_index()


# Merge the result with customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, average_days_between_purchases, on='UserID')


# Drop the 'Days_Between_Purchases' column from the original dataframe 'df'
df.drop('Days_Between_Purchases', axis=1, inplace=True)


customer_engagement


# Assuming 'Transaction Date' is in datetime format
df['Transaction Date'] = pd.to_datetime(df['Transaction Date'])

# Extract month and year from Transaction Date
df['Month'] = df['Transaction Date'].dt.month
df['Year'] = df['Transaction Date'].dt.year

# Calculate monthly spending mean and standard deviation for each user
monthly_spending_stats = df.groupby(['UserID', 'Month'])['Price'].sum().groupby('UserID').agg(['mean', 'std']).reset_index()
monthly_spending_stats.columns = ['UserID', 'Monthly_Spending_Mean', 'Monthly_Spending_Std']

# Calculate yearly spending mean and standard deviation for each user
yearly_spending_stats = df.groupby(['UserID', 'Year'])['Price'].sum().groupby('UserID').agg(['mean', 'std']).reset_index()
yearly_spending_stats.columns = ['UserID', 'Yearly_Spending_Mean', 'Yearly_Spending_Std']

# Merge the results with customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, monthly_spending_stats, on='UserID', how='left')
customer_engagement = pd.merge(customer_engagement, yearly_spending_stats, on='UserID', how='left')

# Calculate spending trend for each user
def calculate_spending_trend(x):
    slope, _, _, _, _ = linregress(np.arange(len(x)), x)
    return slope

spending_trend = df.groupby('UserID')['Price'].apply(calculate_spending_trend).reset_index()
spending_trend.columns = ['UserID', 'Spending_Trend']


# Merge spending trend with customer_engagement dataframe
customer_engagement = pd.merge(customer_engagement, spending_trend, on='UserID')


# Plotting the spending trend
plt.figure(figsize=(15, 6))
plt.scatter(customer_engagement['UserID'], customer_engagement['Spending_Trend'], color='blue')
plt.title('Spending Trend of Customers')
plt.xlabel('User ID')
plt.ylabel('Spending Trend')
plt.grid(True)
plt.show()


customer_engagement


customer_engagement.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 20 entries, 0 to 19
Data columns (total 23 columns):
 #   Column                           Non-Null Count  Dtype  
---  ------                           --------------  -----  
 0   UserID                           20 non-null     object 
 1   Days_Since_Last_Purchase         20 non-null     int64  
 2   Total Transactions               20 non-null     int64  
 3   Total Products Purchased         20 non-null     int32  
 4   Total Products Searched          20 non-null     int32  
 5   Total Products in Wishlist       20 non-null     int32  
 6   Total Products in Cart           20 non-null     int32  
 7   Total Products in Click History  20 non-null     int32  
 8   Total Spend                      20 non-null     float64
 9   Willing to Spend                 20 non-null     float64
 10  Average Transaction Value        20 non-null     float64
 11  Unique Products Purchased        20 non-null     int64  
 12  Unique Products to be Purchased  20 non-null     int64  
 13  Same_Age_or_Above                20 non-null     int64  
 14  Is_Male                          20 non-null     int64  
 15  Buying_Same_Item                 20 non-null     int64  
 16  From_Same_Location               20 non-null     int64  
 17  Days_Between_Purchases           20 non-null     float64
 18  Monthly_Spending_Mean            20 non-null     float64
 19  Monthly_Spending_Std             20 non-null     float64
 20  Yearly_Spending_Mean             20 non-null     float64
 21  Yearly_Spending_Std              20 non-null     float64
 22  Spending_Trend                   20 non-null     float64
dtypes: float64(9), int32(5), int64(8), object(1)
memory usage: 3.4+ KB


customer_engagement.describe().T

df


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 19 columns):
 #   Column             Non-Null Count  Dtype         
---  ------             --------------  -----         
 0   UserID             1000 non-null   object        
 1   Age                1000 non-null   int64         
 2   Gender             1000 non-null   object        
 3   Location           1000 non-null   object        
 4   Item Name          1000 non-null   object        
 5   Brand              1000 non-null   object        
 6   Price              1000 non-null   float64       
 7   Rating             1000 non-null   float64       
 8   Number of Reviews  1000 non-null   int64         
 9   Purchase History   1000 non-null   int32         
 10  Search History     1000 non-null   int32         
 11  Wishlist           1000 non-null   int32         
 12  Cart Items         1000 non-null   int32         
 13  Click History      1000 non-null   int32         
 14  Transaction No.    1000 non-null   int64         
 15  Transaction Date   1000 non-null   datetime64[ns]
 16  Transaction Day    1000 non-null   object        
 17  Month              1000 non-null   int64         
 18  Year               1000 non-null   int64         
dtypes: datetime64[ns](1), float64(2), int32(5), int64(5), object(6)
memory usage: 129.0+ KB


# Plotting box plots for each feature
plt.figure(figsize=(15, 15))
for i, column in enumerate(customer_engagement.columns[1:], 1):
    plt.subplot(5, 5, i)
    customer_engagement.boxplot(column=column)
    plt.title(column)

plt.tight_layout()
plt.show()


# Initializing the IsolationForest model with a contamination parameter of 0.05
model = IsolationForest(contamination=0.05, random_state=0)

# Fitting the model on our dataset (converting DataFrame to NumPy to avoid warning)
customer_engagement['Outlier_Scores'] = model.fit_predict(customer_engagement.iloc[:, 1:].to_numpy())

# Creating a new column to identify outliers (1 for inliers and -1 for outliers)
customer_engagement['Is_Outlier'] = [1 if x == -1 else 0 for x in customer_engagement['Outlier_Scores']]


customer_engagement


# Calculate the percentage of inliers and outliers
outlier_percentage = customer_engagement['Is_Outlier'].value_counts(normalize=True) * 100


outlier_percentage

0    95.0
1     5.0
Name: Is_Outlier, dtype: float64


# Plotting the percentage of inliers and outliers
plt.figure(figsize=(12, 4))
outlier_percentage.plot(kind='barh', color=['#4287f5', '#ff0000'])

# Adding the percentage labels on the bars
for index, value in enumerate(outlier_percentage):
    plt.text(value, index, f'{value:.2f}%', fontsize=15)

plt.title('Percentage of Inliers and Outliers')
plt.xticks(ticks=np.arange(0, 115, 5))
plt.xlabel('Percentage (%)')
plt.ylabel('Is Outlier')
plt.gca().invert_yaxis()
plt.show()


# Separate the outliers for analysis
outliers_data = customer_engagement[customer_engagement['Is_Outlier'] == 1]

# Remove the outliers from the main dataset
customer_engagement_cleaned = customer_engagement[customer_engagement['Is_Outlier'] == 0]

# Drop the 'Outlier_Scores' and 'Is_Outlier' columns
customer_engagement_cleaned = customer_engagement_cleaned.drop(columns=['Outlier_Scores', 'Is_Outlier'])

# Reset the index of the cleaned data
customer_engagement_cleaned.reset_index(drop=True, inplace=True)


# Getting the number of rows in the cleaned customer dataset
customer_engagement_cleaned.shape[0]

19


customer_engagement_cleaned


# Reset background style
sns.set_style('whitegrid')

# Calculate the correlation matrix excluding the 'UserID' column
corr = customer_engagement_cleaned.drop(columns=['UserID']).corr()

# Define a custom colormap
colors = ['#ff0000', '#ffcccc', 'white', '#ffcccc', '#ff0000']
my_cmap = LinearSegmentedColormap.from_list('custom_map', colors, N=256)

# Create a mask to only show the lower triangle of the matrix (since it's mirrored around its top-left to bottom-right diagonal)
mask = np.zeros_like(corr)
mask[np.triu_indices_from(mask, k=1)] = True

# Plot the heatmap
plt.figure(figsize=(12, 10))
sns.heatmap(corr, mask=mask, cmap=my_cmap, annot=True, center=0, fmt='.2f', linewidths=2)
plt.title('Correlation Matrix', fontsize=14)
plt.show()


# Initialize the StandardScaler
scaler = StandardScaler()

# List of columns that don't need to be scaled
columns_to_exclude = ['UserID', 'Unique Products Purchased', 'Unique Products to be Purchased', 'Same_Age_or_Above', 'Is_Male', 'Buying_Same_Item', 'From_Same_Location']

# List of columns that need to be scaled
columns_to_scale = customer_engagement_cleaned.columns.difference(columns_to_exclude)

# Copy the cleaned dataset
customer_engagement_scaled = customer_engagement_cleaned.copy()

# Applying the scaler to the necessary columns in the dataset
customer_engagement_scaled[columns_to_scale] = scaler.fit_transform(customer_engagement_scaled[columns_to_scale])


customer_engagement_scaled


# Setting UserID as the index column
customer_engagement_scaled.set_index('UserID', inplace=True)

# Apply PCA
pca = PCA().fit(customer_engagement_scaled)

# Calculate the Cumulative Sum of the Explained Variance
explained_variance_ratio = pca.explained_variance_ratio_
cumulative_explained_variance = np.cumsum(explained_variance_ratio)

# Set the optimal k value
optimal_k = 6

# Set seaborn plot style
sns.set(rc={'axes.facecolor': '#fcf0dc'}, style='darkgrid')

# Plot the cumulative explained variance against the number of components
plt.figure(figsize=(20, 10))

# Bar chart for the explained variance of each component
barplot = sns.barplot(x=list(range(1, len(cumulative_explained_variance) + 1)), y=explained_variance_ratio, color='#fcc36d', alpha=0.8)

# Line plot for the cumulative explained variance
lineplot, = plt.plot(range(0, len(cumulative_explained_variance)), cumulative_explained_variance, marker='o', linestyle='--', color='#ff6200', linewidth=2)

# Plot optimal k value line
optimal_k_line = plt.axvline(optimal_k - 1, color='red', linestyle='--', label=f'Optimal k value = {optimal_k}') 

# Set labels and title
plt.xlabel('Number of Components', fontsize=14)
plt.ylabel('Explained Variance', fontsize=14)
plt.title('Cumulative Variance vs. Number of Components', fontsize=18)

# Customize ticks and legend
plt.xticks(range(0, len(cumulative_explained_variance)))
plt.legend(handles=[barplot.patches[0], lineplot, optimal_k_line], labels=['Explained Variance of Each Component', 'Cumulative Explained Variance', f'Optimal k value = {optimal_k}'], loc=(0.62, 0.1), frameon=True, framealpha=1.0,   edgecolor='#ff6200')  

# Display the variance values for both graphs on the plots
x_offset = -0.3
y_offset = 0.01
for i, (ev_ratio, cum_ev_ratio) in enumerate(zip(explained_variance_ratio, cumulative_explained_variance)):
    plt.text(i, ev_ratio, f"{ev_ratio:.2f}", ha="center", va="bottom", fontsize=10)
    if i > 0:
        plt.text(i + x_offset, cum_ev_ratio + y_offset, f"{cum_ev_ratio:.2f}", ha="center", va="bottom", fontsize=10)

plt.grid(axis='both')   
plt.show()


# Creating a PCA object with 6 components
pca = PCA(n_components=6)

# Fitting and transforming the original data to the new PCA dataframe
customer_engagement_pca = pca.fit_transform(customer_engagement_scaled)

# Creating a new dataframe from the PCA dataframe, with columns labeled PC1, PC2, etc.
customer_engagement_pca = pd.DataFrame(customer_engagement_pca, columns=['PC'+str(i+1) for i in range(pca.n_components_)])

# Adding the UserID index back to the new PCA dataframe
customer_engagement_pca.index = customer_engagement_scaled.index


customer_engagement_pca


# Define a function to highlight the top 3 absolute values in each column of a dataframe
def top_3(column):
    top3 = column.abs().nlargest(3).index
    return ['background-color:  #ffeacc' if i in top3 else '' for i in column.index]

# Create the PCA component DataFrame and apply the highlighting function
pc_df = pd.DataFrame(pca.components_.T, columns=['PC{}'.format(i+1) for i in range(pca.n_components_)],  
                     index=customer_engagement_scaled.columns)

pc_df.style.apply(top_3, axis=0)


# Set plot style, and background color
sns.set(style='darkgrid', rc={'axes.facecolor': '#fcf0dc'})

# Set the color palette for the plot
sns.set_palette(['#ff6200'])

# Instantiate the clustering model with the specified parameters
km = KMeans(init='k-means++', n_init=10, max_iter=100, random_state=0)

# Create a figure and axis with the desired size
fig, ax = plt.subplots(figsize=(12, 5))

# Instantiate the KElbowVisualizer with the model and range of k values, and disable the timing plot
visualizer = KElbowVisualizer(km, k=(2, 15), timings=False, ax=ax)

# Fit the data to the visualizer
visualizer.fit(customer_engagement_pca)

# Finalize and render the figure
visualizer.show();


# Perform Silhouette analysis for a range of k values and visualize the results.
def silhouette_analysis(df, start_k, stop_k, figsize=(15, 16)):
    # Set the size of the figure
    plt.figure(figsize=figsize)

    # Create a grid with (stop_k - start_k + 1) rows and 2 columns
    grid = gridspec.GridSpec(stop_k - start_k + 1, 2)

    # Assign the first plot to the first row and both columns
    first_plot = plt.subplot(grid[0, :])

    # First plot: Silhouette scores for different k values
    sns.set_palette(['darkorange'])

    silhouette_scores = []

    # Iterate through the range of k values
    for k in range(start_k, stop_k + 1):
        km = KMeans(n_clusters=k, init='k-means++', n_init=10, max_iter=100, random_state=0)
        km.fit(df)
        labels = km.predict(df)
        score = silhouette_score(df, labels)
        silhouette_scores.append(score)

    best_k = start_k + silhouette_scores.index(max(silhouette_scores))

    plt.plot(range(start_k, stop_k + 1), silhouette_scores, marker='o')
    plt.xticks(range(start_k, stop_k + 1))
    plt.xlabel('Number of clusters (k)')
    plt.ylabel('Silhouette score')
    plt.title('Average Silhouette Score for Different k Values', fontsize=15)

    # Add the optimal k value text to the plot
    optimal_k_text = f'The k value with the highest Silhouette score is: {best_k}'
    plt.text(10, 0.23, optimal_k_text, fontsize=12, verticalalignment='bottom', horizontalalignment='left', bbox=dict(facecolor='#fcc36d', edgecolor='#ff6200', boxstyle='round, pad=0.5'))

    # Second plot (subplot): Silhouette plots for each k value
    colors = sns.color_palette("bright")

    for i in range(start_k, stop_k + 1):    
        km = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=100, random_state=0)
        row_idx, col_idx = divmod(i - start_k, 2)

        # Assign the plots to the second, third, and fourth rows
        ax = plt.subplot(grid[row_idx + 1, col_idx])

        visualizer = SilhouetteVisualizer(km, colors=colors, ax=ax)
        visualizer.fit(df)

        # Add the Silhouette score text to the plot
        score = silhouette_score(df, km.labels_)
        ax.text(0.97, 0.02, f'Silhouette Score: {score:.2f}', fontsize=12, ha='right', transform=ax.transAxes, color='red')

        ax.set_title(f'Silhouette Plot for {i} Clusters', fontsize=15)

    plt.tight_layout()
    plt.show()


silhouette_analysis(customer_engagement_pca, 3, 12, figsize=(20, 50))


# Apply KMeans clustering using the optimal k
kmeans = KMeans(n_clusters=4, init='k-means++', n_init=10, max_iter=100, random_state=0)
kmeans.fit(customer_engagement_pca)

# Get the frequency of each cluster
cluster_frequencies = Counter(kmeans.labels_)

# Create a mapping from old labels to new labels based on frequency
label_mapping = {label: new_label for new_label, (label, _) in enumerate(cluster_frequencies.most_common())}

# Reverse the mapping to assign labels as per your criteria
label_mapping = {v: k for k, v in {3: 2, 2: 1, 1: 0, 0: 3}.items()}

# Apply the mapping to get the new labels
new_labels = np.array([label_mapping[label] for label in kmeans.labels_])

# Append the new cluster labels back to the original dataset
customer_engagement_cleaned['cluster'] = new_labels

# Append the new cluster labels to the PCA version of the dataset
customer_engagement_pca['cluster'] = new_labels


customer_engagement_cleaned


customer_engagement_pca


# Setting up the color scheme for the clusters (RGB order)
colors = ['#e8000b', '#1ac938', '#023eff', '#ff7c00']

# Create separate data frames for each cluster
cluster_0 = customer_engagement_pca[customer_engagement_pca['cluster'] == 0]
cluster_1 = customer_engagement_pca[customer_engagement_pca['cluster'] == 1]
cluster_2 = customer_engagement_pca[customer_engagement_pca['cluster'] == 2]
cluster_3 = customer_engagement_pca[customer_engagement_pca['cluster'] == 3]

# Create a 3D scatter plot
fig = go.Figure()

# Add data points for each cluster separately and specify the color
fig.add_trace(go.Scatter3d(x=cluster_0['PC1'], y=cluster_0['PC2'], z=cluster_0['PC3'], mode='markers', marker=dict(color=colors[0], size=5, opacity=0.4), name='Cluster 0'))
fig.add_trace(go.Scatter3d(x=cluster_1['PC1'], y=cluster_1['PC2'], z=cluster_1['PC3'], mode='markers', marker=dict(color=colors[1], size=5, opacity=0.4), name='Cluster 1'))
fig.add_trace(go.Scatter3d(x=cluster_2['PC1'], y=cluster_2['PC2'], z=cluster_2['PC3'], mode='markers', marker=dict(color=colors[2], size=5, opacity=0.4), name='Cluster 2'))
fig.add_trace(go.Scatter3d(x=cluster_3['PC1'], y=cluster_3['PC2'], z=cluster_3['PC3'], mode='markers', marker=dict(color=colors[3], size=5, opacity=0.4), name='Cluster 3'))

# Set the title and layout details
fig.update_layout(
    title=dict(text='3D Visualization of Customer Clusters in PCA Space with 4 Clusters', x=0.5),
    scene=dict(
        xaxis=dict(backgroundcolor="#fcf0dc", gridcolor='white', title='PC1'),
        yaxis=dict(backgroundcolor="#fcf0dc", gridcolor='white', title='PC2'),
        zaxis=dict(backgroundcolor="#fcf0dc", gridcolor='white', title='PC3'),
    ),
    width=900,
    height=800
)

# Show the plot
fig.show()


# Calculate the percentage of customers in each cluster
cluster_percentage = (customer_engagement_pca['cluster'].value_counts(normalize=True) * 100).reset_index()
cluster_percentage.columns = ['Cluster', 'Percentage']
cluster_percentage.sort_values(by='Cluster', inplace=True)

# Create a horizontal bar plot
plt.figure(figsize=(10, 4))
sns.barplot(x='Percentage', y='Cluster', data=cluster_percentage, orient='h', palette=colors)

# Adding percentages on the bars
for index, value in enumerate(cluster_percentage['Percentage']):
    plt.text(value+0.5, index, f'{value:.2f}%')

plt.title('Distribution of Customers Across Clusters', fontsize=14)
plt.xticks(ticks=np.arange(0, 50, 5))
plt.xlabel('Percentage (%)')

# Show the plot
plt.show()


# Compute number of customers
num_observations = len(customer_engagement_pca)

# Separate the features and the cluster labels
X = customer_engagement_pca.drop('cluster', axis=1)
clusters = customer_engagement_pca['cluster']

# Compute the metrics
sil_score = silhouette_score(X, clusters)
calinski_score = calinski_harabasz_score(X, clusters)
davies_score = davies_bouldin_score(X, clusters)

# Create a table to display the metrics and the number of observations
table_data = [
    ["Number of Observations", num_observations],
    ["Silhouette Score", sil_score],
    ["Calinski Harabasz Score", calinski_score],
    ["Davies Bouldin Score", davies_score]
]

# Print the table
print(tabulate(table_data, headers=["Metric", "Value"], tablefmt='pretty'))

+-------------------------+---------------------+
|         Metric          |        Value        |
+-------------------------+---------------------+
| Number of Observations  |         19          |
|    Silhouette Score     | 0.22845646386066745 |
| Calinski Harabasz Score |  5.980821108533767  |
|  Davies Bouldin Score   | 1.0689987453375518  |
+-------------------------+---------------------+


# Setting 'UserID' column as index and assigning it to a new dataframe
df_customer = customer_engagement_cleaned.set_index('UserID')

# Standardize the data (excluding the cluster column)
scaler = StandardScaler()
df_customer_standardized = scaler.fit_transform(df_customer.drop(columns=['cluster'], axis=1))

# Create a new dataframe with standardized values and add the cluster column back
df_customer_standardized = pd.DataFrame(df_customer_standardized, columns=df_customer.columns[:-1], index=df_customer.index)
df_customer_standardized['cluster'] = df_customer['cluster']

# Calculate the centroids of each cluster
cluster_centroids = df_customer_standardized.groupby('cluster').mean()

# Function to create a radar chart
def radar_chart(ax, angles, data, color, cluster):
    # Plot the data and fill the area
    ax.fill(angles, data, color=color, alpha=0.4)
    ax.plot(angles, data, color=color, linewidth=2, linestyle='solid')
    
    # Add a title
    ax.set_title(f'Cluster {cluster}', size=20, color=color, y=1.1)

# Set data
labels=np.array(cluster_centroids.columns)
num_vars = len(labels)

# Compute angle of each axis
angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()

# The plot is circular, so we need to "complete the loop" and append the start to the end
labels = np.concatenate((labels, [labels[0]]))
angles += angles[:1]

# Initialize the figure
fig, ax = plt.subplots(figsize=(35, 25), subplot_kw=dict(polar=True), nrows=1, ncols=4)

# Create radar chart for each cluster
for i, color in enumerate(colors):
    data = cluster_centroids.loc[i].tolist()
    data += data[:1]  # Complete the loop
    radar_chart(ax[i], angles, data, color, i)

# Add input data
ax[0].set_xticks(angles[:-1])
ax[0].set_xticklabels(labels[:-1])

ax[1].set_xticks(angles[:-1])
ax[1].set_xticklabels(labels[:-1])

ax[2].set_xticks(angles[:-1])
ax[2].set_xticklabels(labels[:-1])

ax[3].set_xticks(angles[:-1])
ax[3].set_xticklabels(labels[:-1])

# Add a grid
ax[0].grid(color='grey', linewidth=0.5)

# Display the plot
plt.tight_layout()
plt.show()


# Plot histograms for each feature segmented by the clusters
features = customer_engagement_cleaned.columns[1:-1]
clusters = customer_engagement_cleaned['cluster'].unique()
clusters.sort()

# Setting up the subplots
n_rows = len(features)
n_cols = len(clusters)
fig, axes = plt.subplots(n_rows, n_cols, figsize=(20, 3*n_rows))

# Plotting histograms
for i, feature in enumerate(features):
    for j, cluster in enumerate(clusters):
        data = customer_engagement_cleaned[customer_engagement_cleaned['cluster'] == cluster][feature]
        axes[i, j].hist(data, bins=20, color=colors[j], edgecolor='w', alpha=0.7)
        axes[i, j].set_title(f'Cluster {cluster} - {feature}', fontsize=15)
        axes[i, j].set_xlabel('')
        axes[i, j].set_ylabel('')

# Adjusting layout to prevent overlapping
plt.tight_layout()
plt.show()


#by seeing the plot we can say that cluster 2 is the best cluster


def recommend_brand(item_name):
    # Filter dataset for the given item name
    filtered_data = df[df['Item Name'] == item_name]
    
    if filtered_data.empty:
        return "No recommendation available for this item."
    
    # Count the occurrences of each brand
    brand_counts = filtered_data['Brand'].value_counts()
    
    # Get the top-selling brand
    top_brand = brand_counts.idxmax()
    
    return f"Recommended brand(s) for {item_name}: {top_brand}"

# Main
item_to_search = input("Enter the item name: ")
recommendation = recommend_brand(item_to_search)
print(recommendation)

Enter the item name: Laptop
Recommended brand(s) for Laptop: Thompson Group

	count	mean	std	min	25%	50%	75%	max
Age	1000.0	41.58200	13.829480	18.00	30.000	42.00	54.0000	65.00
Price	1000.0	254.49653	140.606560	10.16	138.135	248.07	377.1825	499.91
Rating	1000.0	2.98440	1.137132	1.00	2.000	2.90	3.9000	5.00
Number of Reviews	1000.0	480.55900	280.554782	1.00	242.500	465.00	703.2500	998.00
Transaction No.	1000.0	500.50000	288.819436	1.00	250.750	500.50	750.2500	1000.00

	Age	Price	Rating	Number of Reviews
count	1000.00000	1000.00000	1000.000000	1000.000000
mean	41.58200	254.49653	2.984400	480.559000
std	13.82948	140.60656	1.137132	280.554782
min	18.00000	10.16000	1.000000	1.000000
25%	30.00000	138.13500	2.000000	242.500000
50%	42.00000	248.07000	2.900000	465.000000
75%	54.00000	377.18250	3.900000	703.250000
max	65.00000	499.91000	5.000000	998.000000

	UserID	Days_Since_Last_Purchase
0	User1	8
1	User10	18
2	User11	8
3	User12	2
4	User13	27
5	User14	13
6	User15	2
7	User16	5
8	User17	3
9	User18	9
10	User19	0
11	User2	31
12	User20	9
13	User3	1
14	User4	19
15	User5	26
16	User6	1
17	User7	2
18	User8	7
19	User9	11

	UserID	Days_Since_Last_Purchase	Total Transactions	Total Products Purchased	Total Products Searched	Total Products in Wishlist	Total Products in Cart	Total Products in Click History	Total Spend	Willing to Spend	...	Same_Age_or_Above	Is_Male	Buying_Same_Item	From_Same_Location	Days_Between_Purchases	Monthly_Spending_Mean	Monthly_Spending_Std	Yearly_Spending_Mean	Yearly_Spending_Std	Spending_Trend
0	User1	8	20	20	25	27	25	27	5776.59	11456.50	...	0	0	0	0	4.448980	1204.861667	585.342892	7229.170	937.156901	-0.684772
1	User10	18	29	29	27	26	32	34	7441.41	10486.52	...	1	1	1	0	8.655172	1260.959167	585.678930	7565.755	4165.969099	0.617309
2	User11	8	26	26	25	27	30	25	5301.64	10131.83	...	1	1	0	0	-0.980000	1006.054167	570.867239	6036.325	1416.426807	3.460524
3	User12	2	17	17	19	22	17	26	4673.99	7863.82	...	1	0	1	0	-5.461538	901.659167	482.868280	5409.955	1166.789829	-4.053605
4	User13	27	35	35	33	35	38	40	8104.59	13273.99	...	1	0	0	0	3.550000	1230.004167	755.513942	7380.025	2622.001442	-1.255994
5	User14	13	31	31	21	22	24	25	9396.11	9997.53	...	1	0	1	0	-4.673913	1107.518333	623.848447	6645.110	2353.010951	-0.295626
6	User15	2	30	30	28	25	29	27	8396.60	11807.39	...	0	0	0	0	-3.981481	1284.206667	933.262190	7705.240	2028.547934	-0.315243
7	User16	5	21	21	23	28	25	22	4820.72	9924.32	...	0	0	0	0	3.304348	953.534167	492.675970	5721.205	264.224591	0.208918
8	User17	3	23	23	26	15	23	17	6739.00	8066.76	...	0	0	0	1	5.725000	893.305000	423.069753	5359.830	386.193440	-0.701129
9	User18	9	19	19	24	23	27	27	5151.60	9230.63	...	0	1	1	0	3.311111	920.897500	347.307700	5525.385	773.398042	2.562159
10	User19	0	22	22	35	32	31	30	5186.49	11015.50	...	0	0	1	0	2.350877	1216.821667	714.442483	7300.930	938.005430	-0.528994
11	User2	31	26	26	31	26	22	21	6909.48	9792.30	...	0	0	0	0	0.120000	1126.255000	749.061419	6757.530	759.347830	-0.094429
12	User20	9	30	30	22	29	23	25	7695.27	9229.10	...	0	1	0	0	5.282609	952.026667	623.558073	5712.160	599.216429	1.430459
13	User3	1	30	30	30	27	24	26	8103.76	9434.37	...	0	0	0	0	-1.020000	1095.616667	681.874269	6573.700	1107.046377	-0.936123
14	User4	19	24	24	20	21	20	23	5797.75	8694.34	...	1	1	0	0	2.809524	890.831667	440.318948	5344.990	785.270365	1.864607
15	User5	26	23	23	20	19	24	22	5589.11	9260.48	...	0	0	0	0	0.186047	942.547500	510.093180	5655.285	912.033397	0.975988
16	User6	1	23	23	22	23	26	22	5605.75	9366.50	...	0	0	0	0	3.163265	1005.869167	670.056435	6035.215	80.560676	-1.027859
17	User7	2	22	22	18	23	19	21	5526.25	7725.81	...	0	0	0	0	-0.813953	812.149167	412.437383	4872.895	704.214714	0.975034
18	User8	7	26	26	36	28	33	32	6121.69	11976.82	...	0	0	0	0	-5.725806	1299.064167	533.605655	7794.385	1187.790900	-0.172598
19	User9	11	22	22	30	28	30	22	4945.00	10629.51	...	0	1	1	0	-4.627451	1103.862500	632.628950	6623.175	1827.185136	-0.027006

	count	mean	std	min	25%	50%	75%	max
Days_Since_Last_Purchase	20.0	10.100000	9.425051	0.000000	2.000000	8.000000	14.250000	31.000000
Total Transactions	20.0	24.950000	4.650693	17.000000	22.000000	23.500000	29.250000	35.000000
Total Products Purchased	20.0	24.950000	4.650693	17.000000	22.000000	23.500000	29.250000	35.000000
Total Products Searched	20.0	25.750000	5.369259	18.000000	21.750000	25.000000	30.000000	36.000000
Total Products in Wishlist	20.0	25.300000	4.508472	15.000000	22.750000	26.000000	28.000000	35.000000
Total Products in Cart	20.0	26.100000	5.179921	17.000000	23.000000	25.000000	30.000000	38.000000
Total Products in Click History	20.0	25.700000	5.232490	17.000000	22.000000	25.000000	27.000000	40.000000
Total Spend	20.0	6364.140000	1387.745692	4673.990000	5272.852500	5787.170000	7504.875000	9396.110000
Willing to Spend	20.0	9968.201000	1439.240817	7725.810000	9230.247500	9858.310000	10726.007500	13273.990000
Average Transaction Value	20.0	255.018795	25.616406	203.909231	235.674563	253.851091	272.087779	303.100323
Unique Products Purchased	20.0	9.100000	1.071153	6.000000	8.750000	9.000000	10.000000	10.000000
Unique Products to be Purchased	20.0	9.850000	0.366348	9.000000	10.000000	10.000000	10.000000	10.000000
Same_Age_or_Above	20.0	0.300000	0.470162	0.000000	0.000000	0.000000	1.000000	1.000000
Is_Male	20.0	0.300000	0.470162	0.000000	0.000000	0.000000	1.000000	1.000000
Buying_Same_Item	20.0	0.300000	0.470162	0.000000	0.000000	0.000000	1.000000	1.000000
From_Same_Location	20.0	0.050000	0.223607	0.000000	0.000000	0.000000	0.000000	1.000000
Days_Between_Purchases	20.0	0.781139	4.139228	-5.725806	-1.760370	1.268462	3.370833	8.655172
Monthly_Spending_Mean	20.0	1060.402208	151.658603	812.149167	937.135000	1050.835417	1207.851667	1299.064167
Monthly_Spending_Std	20.0	588.425607	140.772095	347.307700	490.224047	585.510911	673.010893	933.262190
Yearly_Spending_Mean	20.0	6362.413250	909.951619	4872.895000	5622.810000	6305.012500	7247.110000	7794.385000
Yearly_Spending_Std	20.0	1250.719514	956.976961	80.560676	745.564551	937.581165	1519.116389	4165.969099
Spending_Trend	20.0	0.100081	1.582787	-4.053605	-0.688861	-0.133513	0.975272	3.460524

Importing Necessary Libraries¶

Loading the Dataset¶

Initial Data Analysis¶

Data Cleaning¶

Exploratory Data Analysis (EDA)¶

Feature Engineering¶

Outlier Detection and Treatment¶

Correlation Analysis¶

Feature Scaling¶

Dimensionality Reduction¶

K-Means Clustering¶

Apply Clustering Model K-means:¶

Clustering Evaluation¶

Cluster Analysis and Profiling¶

Brand Recommendation Machine¶

	UserID	Age	Gender	Location	Item Name	Brand	Price	Rating	Number of Reviews	Purchase History	Search History	Wishlist	Cart Items	Click History	Transaction No.	Transaction Date
0	User14	40	Female	Russia	Watch	Henry-Johnson	205.90	2.8	624	True	False	False	False	False	1	2023-07-08 00:00:00
1	User8	50	Male	Russia	T-Shirt	Lawson-Tucker	132.02	3.1	794	False	False	False	True	True	2	2023-03-14 00:00:00
2	User2	59	Male	India	Headphones	Gomez LLC	384.34	1.4	619	False	False	False	False	False	3	2022-10-17 00:00:00
3	User6	48	Female	Argentina	T-Shirt	Morris-Miller	341.77	3.0	336	True	True	True	False	True	4	2022-04-02 00:00:00
4	User5	49	Female	Singapore	Bag	Waters-Hall	309.58	3.6	973	True	True	True	False	False	5	2022-01-29 00:00:00

	count	unique	top	freq
UserID	1000	20	User8	63
Gender	1000	2	Male	530
Location	1000	18	Greece	74
Item Name	1000	10	T-Shirt	124
Brand	1000	969	Smith Inc	3
Transaction Date	1000	548	2022-10-23 00:00:00	6

	UserID	Days_Since_Last_Purchase	Total Transactions	Total Products Purchased	Total Products Searched	Total Products in Wishlist	Total Products in Cart	Total Products in Click History	Total Spend	Willing to Spend	...	Same_Age_or_Above	Is_Male	Buying_Same_Item	From_Same_Location	Days_Between_Purchases	Monthly_Spending_Mean	Monthly_Spending_Std	Yearly_Spending_Mean	Yearly_Spending_Std	Spending_Trend
0	User1	-0.141673	-1.104117	-1.104117	-0.072369	0.568607	-0.108710	0.512360	-0.374045	1.372914	...	0	0	0	0	0.932961	1.048360	0.042255	1.048360	-0.267942	-0.552308
1	User10	1.028672	1.143550	1.143550	0.320492	0.311380	1.497786	2.259640	0.881568	0.571790	...	1	1	1	0	1.961974	1.431774	0.044741	1.431774	3.316030	0.287599
2	User11	-0.141673	0.394327	0.394327	-0.072369	0.568607	1.038787	0.013137	-0.732254	0.278845	...	1	1	0	0	-0.395198	-0.310447	-0.064840	-0.310447	0.264046	2.121612
3	User12	-0.843880	-1.853339	-1.853339	-1.250951	-0.717528	-1.944705	0.262749	-1.205630	-1.594347	...	1	0	1	0	-1.491572	-1.023965	-0.715880	-1.023965	-0.013050	-2.725371
4	User14	0.443499	1.643031	1.643031	-0.858090	-0.717528	-0.338210	0.013137	2.355809	0.167924	...	1	0	1	0	-1.298885	0.383039	0.327129	0.383039	1.303652	-0.301289
5	User15	-0.843880	1.393290	1.393290	0.516922	0.054153	0.809287	0.512360	1.601975	1.662721	...	0	0	0	0	-1.129487	1.590666	2.616255	1.590666	0.943499	-0.313943
6	User16	-0.492777	-0.854376	-0.854376	-0.465230	0.825834	-0.108710	-0.735697	-1.094965	0.107459	...	0	0	0	0	0.652935	-0.669410	-0.643320	-0.669410	-1.014895	0.024166
7	User17	-0.726846	-0.354895	-0.354895	0.124061	-2.518116	-0.567709	-1.983753	0.351808	-1.426735	...	0	0	0	1	1.245129	-1.081064	-1.158286	-1.081064	-0.879510	-0.562859
8	User18	-0.024639	-1.353858	-1.353858	-0.268799	-0.460301	0.350289	0.512360	-0.845415	-0.465473	...	0	1	1	0	0.654590	-0.892475	-1.718794	-0.892475	-0.449714	1.542123
9	User19	-1.077949	-0.604635	-0.604635	1.891934	1.854741	1.268286	1.261194	-0.819101	1.008684	...	0	0	1	0	0.419676	1.130104	0.997368	1.130104	-0.267000	-0.451823
10	User2	2.550120	0.394327	0.394327	1.106213	0.311380	-0.797208	-0.985308	0.480384	-0.001579	...	0	0	0	0	-0.126091	0.511100	1.253488	0.511100	-0.465310	-0.171507
11	User20	-0.024639	1.393290	1.393290	-0.661660	1.083061	-0.567709	0.013137	1.073030	-0.466737	...	0	1	0	0	1.136902	-0.679714	0.324981	-0.679714	-0.643055	0.812121
12	User3	-0.960915	1.393290	1.393290	0.909782	0.568607	-0.338210	0.262749	1.381114	-0.297200	...	0	0	0	0	-0.404983	0.301694	0.756420	0.301694	-0.079365	-0.714441
13	User4	1.145706	-0.105154	-0.105154	-1.054521	-0.974755	-1.256207	-0.486085	-0.358086	-0.908405	...	1	1	0	0	0.531880	-1.097968	-1.030672	-1.097968	-0.436536	1.092168
14	User5	1.964947	-0.354895	-0.354895	-1.054521	-1.489208	-0.338210	-0.735697	-0.515443	-0.440819	...	0	0	0	0	-0.109933	-0.744502	-0.514463	-0.744502	-0.295829	0.518964
15	User6	-0.960915	-0.354895	-0.354895	-0.661660	-0.460301	0.120789	-0.735697	-0.502893	-0.353255	...	0	0	0	0	0.618421	-0.311712	0.668988	-0.311712	-1.218761	-0.773616
16	User7	-0.843880	-0.604635	-0.604635	-1.447381	-0.460301	-1.485707	-0.985308	-0.562852	-1.708332	...	0	0	0	0	-0.354576	-1.635747	-1.236947	-1.635747	-0.526507	0.518349
17	User8	-0.258708	0.394327	0.394327	2.088364	0.825834	1.727285	1.760417	-0.113770	1.802656	...	0	0	0	0	-1.556223	1.692214	-0.340511	1.692214	0.010261	-0.221930
18	User9	0.209430	-0.604635	-0.604635	0.909782	0.825834	1.038787	-0.735697	-1.001233	0.689888	...	0	1	1	0	-1.287519	0.358052	0.392090	0.358052	0.719987	-0.128016

	PC1	PC2	PC3	PC4	PC5	PC6
UserID
User1	0.936563	-1.143190	0.910283	-0.009028	2.010212	1.250703
User10	4.083905	0.884282	-1.529844	3.223612	1.358676	-0.113040
User11	0.449232	-1.182670	-2.514097	-0.001027	-1.539772	-1.158315
User12	-4.361374	-0.856840	3.896517	1.770110	-0.579031	-1.073711
User14	1.513844	3.737479	1.196679	1.048500	-0.684657	-0.629263
User15	4.389574	1.534803	1.544999	-0.930572	-0.279368	-0.287863
User16	-1.653292	-1.770690	-0.667237	-1.033714	0.281043	-0.088000
User17	-3.555436	1.687557	0.776401	-0.507149	1.497421	0.931333
User18	-2.388122	-1.352195	-1.020745	1.467038	0.905118	0.044197
User19	2.777201	-3.054716	0.514527	-0.735451	0.937913	-0.113852
User2	0.529988	0.986667	0.545649	-0.362776	-1.723708	2.640775
User20	0.334986	1.792064	-1.835964	-1.354093	0.488958	-0.832498
User3	1.846067	1.609827	0.816904	-1.487781	0.274646	-0.924438
User4	-2.650367	0.879670	-1.954325	0.487366	-0.431188	0.229777
User5	-1.780206	0.592616	-1.448589	0.163298	-0.676998	1.370638
User6	-1.190682	-0.477178	0.430680	-1.438194	0.525688	-0.066020
User7	-3.839584	0.407699	-0.567468	-0.669786	0.034044	-1.241481
User8	3.924105	-2.071793	0.466635	-0.136173	-0.451456	-0.096538
User9	0.633596	-2.203393	0.438995	0.505821	-1.947541	0.157595

	PC1	PC2	PC3	PC4	PC5	PC6
Days_Since_Last_Purchase	0.012421	0.155683	-0.251566	0.307145	-0.366469	0.686618
Total Transactions	0.246420	0.390521	-0.139453	-0.123126	-0.131800	-0.167960
Total Products Purchased	0.246420	0.390521	-0.139453	-0.123126	-0.131800	-0.167960
Total Products Searched	0.268188	-0.218762	0.100864	-0.126280	-0.035974	0.248746
Total Products in Wishlist	0.233460	-0.287695	-0.060023	-0.170468	-0.060605	-0.208527
Total Products in Cart	0.289884	-0.243663	-0.151697	0.084687	0.044878	-0.018738
Total Products in Click History	0.263380	-0.146000	0.006987	0.357851	0.231570	-0.283946
Total Spend	0.193791	0.489730	0.063556	-0.049532	0.055873	-0.056861
Willing to Spend	0.330902	-0.182423	0.024622	-0.032130	-0.006959	0.166456
Average Transaction Value	-0.015456	0.362477	0.365909	0.121861	0.384783	0.181243
Unique Products Purchased	0.258305	0.065222	-0.309175	-0.287152	0.312128	-0.017150
Unique Products to be Purchased	0.075481	-0.053960	0.041539	0.025785	-0.051501	0.004600
Same_Age_or_Above	-0.007249	0.062794	-0.021540	0.244118	-0.087809	-0.157074
Is_Male	0.003481	-0.021444	-0.200295	0.161861	-0.054565	-0.095707
Buying_Same_Item	0.016975	-0.051611	0.083206	0.272202	-0.000446	-0.098899
From_Same_Location	-0.026716	0.030610	0.018478	-0.018963	0.070090	0.053301
Days_Between_Purchases	-0.041774	0.034261	-0.342288	0.036256	0.668519	0.265742
Monthly_Spending_Mean	0.355358	-0.075228	0.150140	0.072504	0.048136	0.177516
Monthly_Spending_Std	0.270756	0.094767	0.191413	-0.288405	-0.193403	0.097605
Yearly_Spending_Mean	0.355358	-0.075228	0.150140	0.072504	0.048136	0.177516
Yearly_Spending_Std	0.223646	0.135225	-0.010937	0.581604	-0.049576	-0.176974
Spending_Trend	0.012840	0.002409	-0.615047	0.011609	-0.109695	-0.027400